Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning a translation lexicon from monolingual corpora
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
A geometric view on bilingual lexicon extraction from comparable corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Exploiting comparable corpora and bilingual dictionaries for cross-language text categorization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Advanced learning algorithms for cross-language patent retrieval and classification
Information Processing and Management: an International Journal
Can chinese web pages be classified with english data source?
Proceedings of the 17th international conference on World Wide Web
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Multilingual spectral clustering using document similarity propagation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Multilingual text classification using ontologies
ECIR'07 Proceedings of the 29th European conference on IR research
Bilingual news clustering using named entities and fuzzy similarity
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Cross-language text classification using structural correspondence learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Sentiment analysis with a multilingual pipeline
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Bilingual co-training for sentiment classification of chinese product reviews
Computational Linguistics
Multilingual news document clustering: two algorithms based on cognate named entities
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Active learning for cross language text categorization
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A Comparative Study of Cross-Lingual Sentiment Classification
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Unsupervised feature adaptation for cross-domain NLP with an application to compositionality grading
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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In a multilingual scenario, the classical monolingual text categorization problem can be reformulated as a cross language TC task, in which we have to cope with two or more languages (e.g. English and Italian). In this setting, the system is trained using labeled examples in a source language (e.g. English), and it classifies documents in a different target language (e.g. Italian). In this paper we propose a novel approach to solve the cross language text categorization problem based on acquiring Multilingual Domain Models from comparable corpora in a totally unsupervised way and without using any external knowledge source (e.g. bilingual dictionaries). These Multilingual Domain Models are exploited to define a generalized similarity function (i.e. a kernel function) among documents in different languages, which is used inside a Support Vector Machines classification framework. The results show that our approach is a feasible and cheap solution that largely outperforms a baseline.